• Journal of applied mathematics & informatics
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• 제41권6호
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• pp.1275-1301
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• 2023

This paper introduced the Weibull Marshall-Olkin Power Lomax (WMOPL) distribution. The statistical aspects of the proposed model are presented, such as the quantiles function, moments, mean residual life and mean deviations, variance, skewness, kurtosis, and reliability measures like the residual life function, and stress-strength reliability. The parameters of the new model are estimated using six different methods, and simulation research is illustrated to compare the six estimation methods. In the end, two real data sets show that the Weibull Marshall-Olkin Power Lomax distribution is flexible and suitable for modeling data.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1016-1026
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• 2017

An adaptive extended Kalman filter based on the maximum likelihood (EKF-ML) is proposed for detecting voltage sag in this paper. Considering that the choice of the process and measurement error covariance matrices affects seriously the performance of the extended Kalman filter (EKF), the EKF-ML method uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. This can ensure that the EKF has better accuracy and faster convergence for estimating the voltage amplitude (states). Moreover, without more complexity, the EKF-ML algorithm is almost as simple as the conventional EKF, but it has better anti-disturbance performance and more accuracy in detection of the voltage sag. More importantly, the EKF-ML algorithm is capable of accurately estimating the noise parameters and is robust against various noise levels. Simulation results show that the proposed method performs with a fast dynamic and tracking response, when voltage signals contain harmonics or a pulse and are jointly embedded in an unknown measurement noise.

• 한국통신학회논문지
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• 제32권5A호
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• pp.409-416
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• 2007

In cellular orthogonal frequency division multiplexing (OFDM) networks, co-channel interference (CCI) leads to severe degradation in the BER performance. To solve this problem, maximum-likelihood estimation (MLE) CCI cancellation scheme has been proposed in the literature. MLE CCI cancellation scheme generates weighted replicas of the transmitted signals where weights represent the estimated channel transfer functions. The replica with the smallest Euclidean distance from the received signal is selected and data are detected. When the received power of the desired and interference signals are nearly the same, the BER performance is degraded. In this paper, we propose a closed-loop power control (PC) scheme capable of detecting the equal received power situation at the mobile station (MS) receiver by using the newly introduced parameter power ratio (PR). When this situation is detected, the MS sends a feedback to the desired base station (BS) which boosts the transmission power in the next frame. At cell edge where signal to interferer ratio (SIR) is considered to have average value between -5 dB and 10 dB, computer simulations show that the proposed CCI cancellation scheme has a gain of 7 dB at 28 Km/h.

• 산업경영시스템학회지
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• 제39권1호
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.181-191
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• 2018

Rapid development of information technology makes our environment become smarter and massive high performance computers are providing powerful computing for that. Graphics Processing Unit (GPU) as a typical high performance component is being widely used for both graphics and general-purpose applications. Although it can greatly improve computing power, it also delivers significant power consumption and need sufficient power supplies. To make high performance computing more sustainable, the important step is to measure it. Current power technologies for GPU have some drawbacks, such as they are not applicable for power estimation at the early stage. In this article, we present a novel power technology to correlate power consumption and the characteristics at the programmer perspective, and then to estimate power consumption of source program without prerunning. We conduct experiments on Nvidia's GT740 platform; the results show that our power model is more accurately than regression model and has an average error of 2.34% and the maximum error of 9.65%.

• 전력전자학회논문지
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• 제10권1호
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• pp.13-20
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• 2005

본 논문에서는 영구자석 선형동기전동기의 초기 자극위치를 추정하는 알고리즘을 제안하였다. 이 알고리즘은 전동기 파라미터를 사용하지 않고 두 제어 축에 같은 추력 지령을 인가하여 각 제어 축에 의해서 발생하는 이동거리의 최대 값을 관측하고 관측된 최대 위치 변위의 오차를 PI 제어함으로써 초기 자극 위치를 빠르고 정확하게 찾는 것이다. 제안된 알고리즘을 시뮬레이션과 실험을 통해 타당성을 검증하였다

• 전력전자학회논문지
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• 제12권4호
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• pp.291-299
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• 2007

영구자석 동기전동기를 단위 전류 당 최대 토크 운전 점에서 운전하기 위해서는 전동기의 상전류 및 온도에 따라 변화하는 인덕턴스 및 역기전력 상수와 같은 전동기의 파라미터들에 대한 정확한 값을 알고 있어야 한다. 전동기 파라미터의 온라인 추정을 위한 적응 추정 기법은 정확한 값을 추정하는 데 어려움이 있고 복잡한 수학 연산을 필요로 하기 때문에 실질적인 응용에는 적합하지 못하다. 본 논문의 목적은 느린 동적 부하를 가지는 벡터 제어 영구자석 동기전동기 구동장치를 위한 단순한 단위 전류 당 최대 토크 운전 점 추적 제어 전략을 제안하는 것이다. 제안된 방식은 전류 위상각을 조절하고 지령 전력의 변화를 관측함으로써 단위 전류 당 최대 토크 운전 점들을 찾아간다. 전류 위상각 조절 전략은 부하 변동이 지령 전력에 미치는 영향을 감지할 수 있도록 설계된다. 따라서, 제안된 방식은 부하 변동에 관계없이 영구자석 동기전동기의 단위 전류 당 최대 토크 운전 점들을 추적할 수 있다. 컴퓨터 시뮬레이션과 실험을 통하여 제안된 방식의 효용성을 보인다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.566-569
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• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권3호
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.606-607
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• 2007

This paper is about optimal flywheel capacity estimation for Ullueng-do power system. The power system of Ullueng-do has some differences with other island power system in Korea. It includes wind generator, hydro-generators as well as diesel generators. There are some problems on 600kW wind generator. Because of frequent drop of wind generator, the Ulleung-do power system have been threatened on frequency. The power frequency is 60Hz, and it should be between 59.9 and 60.1Hz. However, since the electrical inertia is small and the weight of wind generation is relatively high, generator drop of wind generation might make the power frequency out of boundary. In this paper, the flywheel energy storage system is assumed to be installed on Ulleung-do power system. Then, the maximum wind generation capacity and the optimal superconducting flywheel energy storage system capacity is estimated by the transient stability simulations.